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Explore nonlinear mixture identification in multiview and self-supervised representation learning. Gain insights into extracting essential information from high-dimensional data for improved AI applications.
Explore emergent concepts in deep neural networks, from image classification to reinforcement learning, revealing inner workings and enabling meaningful human-AI interactions for content creation and robotics control.
Explore recent advancements in Explainable AI for scientific applications, focusing on novel insights and developments presented by Klaus-Robert Müller at IPAM's workshop.
Explores learning-based algorithms for low-rank matrix approximation, focusing on optimizing sketching matrices using training data to significantly reduce approximation errors in efficient computational methods.
Innovative approach to recover 3D atomic structures of flexible macromolecules from cryo-EM data, combining biological knowledge with spectral analysis to estimate conformational variations.
Explores tensor methods for simulating chemical transition processes, focusing on compressing high-dimensional PDE functions and introducing a novel tensor-network generative modeling approach without optimization.
Explore innovative techniques for reconstructing 3D molecular structures from cryo-EM data, focusing on the cryoAI algorithm that combines machine learning and physics-based approaches for efficient processing of large datasets.
Explores innovative computational frameworks for recovering small molecular structures using cryo-EM, challenging conventional limitations and bypassing particle picking for enhanced structural determination.
Explore zero-dose extrapolation in cryoEM, enhancing structural detail and insights into radiation damage for improved molecular visualization and atomic model refinement.
Explore advanced techniques for analyzing molecular conformations in cryo-electron microscopy, focusing on emerging approaches for complex conformational heterogeneity and associated challenges.
Explore machine learning techniques for analyzing cryo-EM images to determine protein structure and dynamics, focusing on the cryoDRGN algorithm and its applications in structural biology.
Explore deep learning methodologies for analyzing CryoEM and CryoET data, focusing on population dynamics and contextual annotation of macromolecules in cellular environments.
Explore ptychographic imaging and set projection algorithms for diffractive imaging, with applications in visible light, X-ray, and electron experiments.
Explore 3D atomic structure analysis of nanomaterials using advanced quantitative TEM techniques, including parameter estimation, deep learning, and fusion of ADF STEM with EDX for heterogeneous materials.
Explore advanced microscopy techniques for analyzing magnesium nanoparticles, including tomography, electron diffraction, and hyperspectral optical spectroscopy, with insights on plasmon resonances and photothermal effects.
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